Automatic extraction of task statements from structured meeting content
Automatic extraction of task statements from structured meeting content
复制标题
从结构化会议内容中自动提取任务陈述
DOI：
10.5220/0005609703070315
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
S. Matsubara
中科院分区：
文献类型：
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作者：
K. Nagao;Keisuke Inoue;Naoya Morita;S. Matsubara
我们以前开发了一个讨论挖掘系统，详细记录面对面的会议，分析其内容，并进行知识发现。通过浏览会议记录等文件回顾过去的讨论内容是开展今后活动的有效手段。在定期讨论某些研究课题的会议上，例如在实验室举行的研讨会上，发言者必须通过从讨论记录中检查紧急事项来讨论未来的问题。我们呼吁声明，包括建议或要求在以前的会议上提出的“任务声明”，并提出了一种方法，自动提取它们。该方法根据语句的语义属性和语言特征，采用最大熵方法建立概率模型。根据语句的概率判断语句是否为任务语句。通过实验验证了该方法的有效性。
We previously developed a discussion mining system that records face-to-face meetings in detail, analyzes their content, and conducts knowledge discovery. Looking back on past discussion content by browsing documents, such as minutes, is an effective means for conducting future activities. In meetings at which some research topics are regularly discussed, such as seminars in laboratories, the presenters are required to discuss future issues by checking urgent matters from the discussion records. We call statements including advice or requests proposed at previous meetings “task statements” and propose a method for automatically extracting them. With this method, based on certain semantic attributes and linguistic characteristics of statements, a probabilistic model is created using the maximum entropy method. A statement is judged whether it is a task statement according to its probability. A seminar-based experiment validated the effectiveness of the proposed extraction method.